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Rötter, R.P.; Appiah, M.; Fichtler, E.; Kersebaum, K.C.; Trnka, M.; Hoffmann, M.P. |
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Title |
Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes-A review |
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Journal Article |
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Year |
2018 |
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Field Crops Research |
Abbreviated Journal |
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221 |
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142-156 |
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Keywords |
ft_macsur; Agroclimatic extremes; Crop model; Heat; Drought; Heavy rain; Anthropogenic Climate-Change; Head-Emergence Frost; Weather Extremes; Wheat Yields; Temperature Variability; Induced Sterility; Food Security; Soil-Moisture; Plant-Growth; Winter-Wheat |
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Climate change implies higher frequency and magnitude of agroclimatic extremes threatening plant production and the provision of other ecosystem services. This review is motivated by a mismatch between advances made regarding deeper understanding of abiotic stress physiology and its incorporation into ecophysiological models in order to more accurately quantifying the impacts of extreme events at crop system or higher aggregation levels. Adverse agroclimatic extremes considered most detrimental to crop production include drought, heat, heavy rains/hail and storm, flooding and frost, and, in particular, combinations of them. Our core question is: How have and could empirical data be exploited to improve the capability of widely used crop simulation models in assessing crop impacts of key agroclimatic extremes for the globally most important grain crops? To date there is no comprehensive review synthesizing available knowledge for a broad range of extremes, grain crops and crop models as a basis for identifying research gaps and prospects. To address these issues, we selected eight major grain crops and performed three systematic reviews using SCOPUS for period 1995-2016. Furthermore, we amended/complemented the reviews manually and performed an in-depth analysis using a sub-sample of papers. Results show that by far the majority of empirical studies (1631 out of 1772) concentrate on the three agroclimatic extremes drought, heat and heavy rain and on the three major staples wheat, maize and rice (1259 out of 1772); the concentration on just a few has increased over time. With respect to modelling studies two model families, i.e. CERES-DSSAT and APSIM, are dearly dominating for wheat and maize; for rice, ORYZA2000 and CERES-Rice predominate and are equally strong. For crops other than maize and wheat the number of studies is small. Empirical and modelling papers don’t differ much in the proportions the various extreme events are dealt with drought and heat stress together account for approx. 80% of the studies. There has been a dramatic increase in the number of papers, especially after 2010. As a way forward, we suggest to have very targeted and well-designed experiments on the specific crop impacts of a given extreme as well as of combinations of them. This in particular refers to extremes addressed with insufficient specificity (e.g. drought) or being under-researched in relation to their economic importance (heavy rains/storm and flooding). Furthermore, we strongly recommend extending research to crops other than wheat, maize and rice. |
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MA @ admin @ |
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5199 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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141-157 |
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crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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Ghaley, B.B.; Vesterdal, L.; Porter, J.R. |
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Quantification and valuation of ecosystem services in diverse production systems for informed decision-making |
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2014 |
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Environmental Science & Policy |
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Environmental Science & Policy |
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39 |
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139-149 |
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bio-physical quantification; combined food and energy system; economic valuation field measurements; land management; marketable and non-marketable ecosystem services; land-use change; carbon; farm; efficiency; crops; china; model; scale; field |
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The empirical evidence of decline in ecosystem services (ES) over the last century has reinforced the call for ES quantification, monitoring and valuation. Usually, only provisioning ES are marketable and accounted for, whereas regulating, supporting and cultural ES are typically non-marketable and overlooked in connection with land-use or management decisions. The objective of this study was to quantify and value total ES (marketable and non-marketable) of diverse production systems and management intensities in Denmark to provide a basis for decisions based on economic values. The production systems were conventional wheat (Cwheat), a combined food and energy (CFE) production system and beech forest. Marketable (provisioning ES) and non-marketable ES (supporting, regulating and cultural) ES were quantified by dedicated on-site field measurements supplemented by literature data. The value of total ES was highest in CFE (US$ 3142 ha(-1) yr(-1)) followed by Cwheat (US$ 2767 ha (1) yr(-1)) and beech forest (US$ 2328 ha(-1) yr(-1)). As the production system shifted from Cwheat – CFE-beech, the marketable ES share decreased from 88% to 75% in CFE and 55% in beech whereas the non-marketable ES share increased to 12%, 25% and 45% of total ES in Cwheat, CFE and beech respectively, demonstrating production system and management effects on ES values. Total ES valuation, disintegrated into marketable and non-marketable share is a potential way forward to value ES and `tune’ our production systems for enhanced ES provision. Such monetary valuation can be used by policy makers and land managers as a tool to assess ES value and monitor the sustained flow of ES. The application of ES-based valuation for land management can enhance ES provision for maintaining the productive capacity of the land without depending on the external fossil-based fertilizer and chemical input. (C) 2013 Elsevier Ltd. All rights reserved. |
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1462-9011 |
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CropM |
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MA @ admin @ |
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4623 |
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De Pascale, S.; Maggio, A.; Orsini, F.; Stanghellini, C.; Heuvelink, E. |
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Growth response and radiation use efficiency in tomato exposed to short-term and long-term salinized soils |
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Journal Article |
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2015 |
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Scientia Horticulturae |
Abbreviated Journal |
Scientia Horticulturae |
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189 |
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139-149 |
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Leaf osmotic adjustment; Stomatal resistance; Leaf water potential; Light; Salt stress; RUE; physiological-response; salt tolerance; drought stress; water-use; yield; nitrogen; interception; productivity; leaf; photosynthesis |
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Farmlands are increasingly exposed to degradation phenomena associated to climate change and agricultural practices, including irrigation. It is estimated that about 20% of the world’s irrigated land is salt affected. In this paper we aimed at evaluating the effect of seasonal and multiannual soil satinization on growth, yield, and radiation use efficiency of tomato in open field. Two field experiments were carried out at the Experimental Station of the University of Naples Federico II (latitude 40 degrees 31’N longitude 14 degrees 58’E) (Italy) on tomato during 2004 and 2005 to study the effect of five levels of water salinity: NSC (EC = 0.5 dS m(-1)), SW1 (EC= 2.3 dS m(-1)), SW2 (EC= 4.4 dS m(-1)), SW3 (EC= 8.5 dS m(-1)) and SW4 (EC= 15.7 dS m(-1)) in a soil exposed to one-season salinization (ST = short-term) and an adjacent soil exposed to >20 years salinization (LT = long-term). Plant growth, yield and fruit quality (pH, EC, total soluble solids and the concentration of reducing sugars and of titratable acids), and plant water relations were measured and radiation use efficiency (RUE) was calculated. Increasing water salinity negatively affected the leaf area index (LAI), radiation use efficiency (RUE) and above-ground dry weight (DW) accumulation resulting in lower total and marketable yield. Maximum total and marketable yield obtained with the NSC treatment were respectively 117.9 and 111.0 Mg ha(-1) in 2004 and 113.1 and 107.9 Mg ha(-1) in 2005. Although the smaller leaf area of salinized plants was largely responsible for reduced RUE, we found approximately 50% of this reduction to be accounted for by processes other than changed crop architecture. These may include an increased stomatal resistance, increased mesophyll resistance and other impaired metabolic functions that may occur at high salinity. Remarkably, we found that LT salinized plants had a slightly better efficiency of use of intercepted radiation (RUEIR) at a given EC of soil extract than ST salinized plants indicating that LT salinization, and consequent permanent modifications of the soil physical properties, may trigger additional physiological mechanisms of adaptation compared to ST salinized plants. These differences are relevant in light of the evolution of salinized areas, also in response to climate change. |
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0304-4238 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4557 |
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Klosterhalfen, A.; Herbst, M.; Weihermueller, L.; Graf, A.; Schmidt, M.; Stadler, A.; Schneider, K.; Subke, J.-A.; Huisman, J.A.; Vereecken, H. |
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Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands |
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Journal Article |
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2017 |
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Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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363 |
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137-156 |
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AgroC; Soil respiration; Carbon balance; Winter wheat; Grassland; NEE; LOLIUM-PERENNE L; SOIL HETEROTROPHIC RESPIRATION; LAND-SURFACE MODELS; EDDY-COVARIANCE; WINTER-WHEAT; CARBOHYDRATE CONTENT; TURNOVER MODEL; ROTHC MODEL; ROOT RATIOS; CO2 EFFLUX |
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Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements. (C) 2017 Elsevier B.V. All rights reserved. |
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2017-11-09 |
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0304-3800 |
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CropM, ft_MACSUR |
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MA @ admin @ |
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5216 |
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